Manjeet Dahiya

Manjeet Dahiya Welcome to the blog page of Manjeet Dahiya! Manjeet Dahiya is a Sr. Data Scientist with Delhivery. He has earlier worked with Agilent Technologies and United Online (Juno Online). Manjeet obtained his PhD in computer science from IIT Delhi, and BTech in electrical engineering from IIT Kanpur.

Manjeet writes articles/notes on data science, machine learning and AI, probability and statistics, programming languages, and computer science in general. Following are a few recent articles. For the complete list, checkout all posts.

Recent posts:

Parametric vs Non-parametric Models

One of the criteria to classify machine learning or statistical learning approaches is parametric vs non-parametric models. This post presents the contrast between the two.

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Lagrange Multiplier and Lagrangian

Lagrange multiplier is a technique to determine a local minima or maxima of an objective function subject to some constraint. It allow us to convert the given objective function and the constraint into a single objective function, which we can use with traditional techniques, like equating the derivative to zero, to determine a local minima or maxima.

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Expectation and Various Summaries of a Distribution

The distribution of a random variable is quite detailed — it contains all the probabilistic information of the random variable. Analogous to the summaries of the sample data sets like mean and variance, we can define summaries of the probability distributions. This post presents various summaries or properties of probability distributions.

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